Thermal compensation

ABSTRACT

Method of characterizing particles suspended in a fluid dispersant by light diffraction, comprising: obtaining measurement data from a detector element, the detector element being arranged to measure the intensity of scattered light; identifying a measurement contribution arising from light scattered by inhomogeneities in the dispersant; processing the measurement data to remove or separate the measurement contribution arising from light scattered by inhomogeneities in the dispersant; calculating a particle size distribution from the processed measurement. The detector element is one of a plurality of detector elements from which the measurement data is obtained. The detector elements are arranged to measure the intensity of scattered light at a plurality of scattering angles, the plurality of scattering angles distributed over a plurality of angles about an illumination axis. Identifying a measurement contribution arising from light scattered by inhomogeneities in the dispersant comprises identifying measured scattered light that is asymmetric about the illumination axis.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/668,552, filed Aug. 3, 2017, which claims priority to EuropeanApplication No. 16182896.7, filed on Aug. 4, 2016, the entire contentsof each of which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The invention relates to a particle characterization instrument, and toa method of particle characterization.

BACKGROUND OF THE INVENTION

A diffraction based particle characterization instrument works bymeasuring light scattered from particles. A mathematical description (ormodel) of the relationship between scattering patterns and particle sizedistribution is used to infer the particle size distribution. Themathematical description may require, as a calculation parameter, theratio of refractive index between the particle and the medium in whichthe particle is suspended. By comparing measured light scattering datato the model a particle size distribution (PSD) may be calculated. Thetheory that is typically used assumes that the refractive index of themedium is homogenous and static. If the refractive index of the mediumis not homogeneous and static during measurement the calculated PSD mayinclude spurious sizes.

Both diffraction based measurements (such as static light scattering)and dynamic light scattering (based on temporal characteristics ofscattering) are affected by scattering from inhomogeneities in thediluent medium.

There are several reasons the refractive index of the suspending medium(or dispersant) may be inhomogeneous. The most common is due to thermalvariations in the dispersant but other causes can include pressurevariations, contaminants, sample dissolution, etc. The scatteringpattern caused by these kinds of inhomogeneities may be substantiallyrandom in nature, and therefore difficult to remove from measurementdata.

JP2015/141025 discloses a device in which isotropic background light issubtracted to allow a scattering measurement to be performed without afilter to remove fluorescent light. U.S. Pat. No. 7,471,393 discloses aninstrument for measuring the size distribution of a particle sample bycounting and classifying particles into selected size ranges.

Conventionally, the effect of thermal or pressure variations areminimised by waiting for such variations to equalize while holding thesample in a temperature controlled environment. However, conditions canexist where this takes an excessive amount of time or even wheresettling will not occur. For contaminants or dissolution there is noknown general solution.

SUMMARY OF THE INVENTION

According to a first aspect of the invention, there is provided a methodof characterizing particles suspended in a fluid dispersant by lightdiffraction, comprising:

obtaining measurement data from a detector element, the detector elementarranged to measure the intensity of scattered light;

identifying a measurement contribution arising from light scattered byinhomogeneities in the dispersant;

processing the measurement data to remove or separate the measurementcontribution arising from light scattered by inhomogeneities in thedispersant;

calculating a particle size distribution from the processed measurement.

The detector element may be one of a plurality of detector elements fromwhich the measurement data is obtained.

The detector elements may be arranged to measure the intensity ofscattered light at a plurality of scattering angles, the plurality ofscattering angles distributed over a plurality of angles about anillumination axis

Calculating a particle size distribution may comprise performing acalculation based on the diffraction pattern of light at differentangles (e.g. static light scattering), and/or may comprise performing anautocorrelation on the measurement data (e.g. dynamic light scattering).

Identifying a measurement contribution arising from light scattered byinhomogeneities in the dispersant may comprise identifying measuredscattered light that is asymmetric about the illumination axis.

Identifying measured scattered light that is asymmetric about theillumination axis may comprise identifying whether the scattered lightis sufficiently asymmetric about the illumination axis to suggest thatit does not arise from scattering from a particle. A small degree ofasymmetry about the illumination axis may not mean that light isscattered from a dispersant inhomogeneity. Identifying measuredscattered light that is asymmetric about the illumination axis maycomprise any data processing that identifies data that is moreasymmetric than would be expected to arise from scattering from aparticle.

In some embodiments the method need not include actually performing themeasurement that provides the measurement data. Obtaining themeasurement data may include reading the measurement data from a storagemedium (e.g. non-volatile memory, flash drive, hard disk, etc.) orreceiving the measurement data over a communications network.

At least some of the plurality of scattering angles may be alternatelyarranged between a first and second radial location about theillumination axis with increasing scattering angle.

The detector elements must be sensitive to asymmetry about theillumination axis. The first and second radial location about theillumination axis may be separated by at least 90 degrees about theillumination axis.

Viewed along the illumination axis, each detector element may beconsidered to lie on a radius drawn from the illumination axis throughthe centroid of the detector element. The location of the detectorelement along the illumination axis is not relevant to determining thedegree of angular separation about illumination axis, so the respectiveradial locations of the detectors can be considered with reference totheir projection on a virtual plane, normal to the illumination axis, atan arbitrary location. The degree of angular separation betweenrespective detector elements is therefore the angle between theirrespective radial locations in the virtual plane.

At least some of the plurality of scattering angles may be a logarithmicseries of scattering angles. At least some of the detector elements maybe arranged with their centroids in a logarithmic series of scatteringangles.

Obtaining a measurement may comprise obtaining a time history of theintensity of scattered light from the detector element, or from thedetector elements (e.g. at the plurality of scattering angles).

Identifying a measurement contribution arising from light scattered byinhomogeneities in the dispersant may comprise identifying peaks in themeasurements for each of the plurality of scattering angles.

Identifying peaks may comprise comparing measurement data with asmoothed data (such as a moving average, or filtered data) obtained fromthe same measurement data.

Where the smoothed data comprises a moving average, the moving averagemay be obtained from a plurality of moving averages with differenttemporal width.

The method may comprise classifying the peaks as: a particle peak,resulting from scattering from a particle; or a spurious peak, resultingfrom scattering from dispersant inhomogeneities.

A combination of temporal and symmetry characteristics can be used toidentify and reject spurious peaks.

The method may comprise classifying a peak as a particle peak whencorresponding peaks are present within a period t_(w) over a continuousrange of n detectors with adjacent scattering angles, at least some ofthe n detectors having angular separation about the illumination axis.

Processing the data may comprise discarding measurement data taken attimes when a measurement contribution arising from light scattered byinhomogeneities in the dispersant is present. The data discarded maycomprise only those scattering angles that include a measurementcontribution arising from light scattered by inhomogeneities in thedispersant.

Processing the data may comprise removing a spurious peak by replacingthe spurious peak with a moving average.

The method may further comprise performing a diffraction experiment(including illuminating a sample and detecting scattered light) toobtain the measurement data.

According to a second aspect, there is provided a processor orinstrument configured to perform the method of the first aspect.

According to a third aspect, there is provided a machine readable,non-transient storage medium, comprising instructions for configuring aprocessor or instrument to perform the method according to the firstaspect.

Features of each and every aspect may be combined with those of each andevery other aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will be described, purely by way of example, with referenceto the accompanying drawings, in which:

FIG. 1 is a schematic drawing of a particle characterization instrumentconfigured in accordance with an embodiment;

FIG. 2 is a schematic of a detector array;

FIG. 3 is a diagram illustrating angular separation of detector elementsabout the illumination axis;

FIG. 4 illustrates the identification of a particle peak in measurementdata; and

FIG. 5 illustrates the identification of a spurious peak in measurementdata.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1, a laser diffraction particle characterizationinstrument 100 is shown, comprising a light source 101, sample cell 103,detector 107 and processor 110. The light source 101 may be a lasersource, and is operable to illuminate a sample within the sample cell103 along an illumination axis 102. The sample comprises particles 105suspended in a dispersant fluid 104 (e.g. water).

The interaction of the illuminating light beam with the particles 105results in scattering/diffraction, producing scattered light 106. Thedetector 107 is arranged to detect the scattered light. A plurality ofdetector elements are provided, arranged to receive light scattered atdifferent scattering angles (relative to the illumination axis 102). Thedetector elements 107 are also distributed about the illumination axis102 at different angles. The detector 107 may comprise an array ofdetector elements (e.g. a focal plane array detector) or may comprise aplurality of discrete (e.g. spaced apart) detector elements.

The scatter pattern projected from the particles 105 onto the detector107 is symmetric about the illumination axis 102 for particle sizeswhere polarisation effects are insignificant. In practice this typicallymeans that the scatter pattern is symmetric about the illumination axisfor particle sizes greater than about 10 μm.

Referring to FIG. 2, an example detector focal plane array 107 is shown,comprising a plurality of detector elements 107 a-f (only the largerelements are labelled for clarity). The detector elements arealternately arranged between a first and second angle about theillumination axis 102 with increasing scattering angle. In the exampleof FIG. 2, the first and second angle about the illumination axis 102are 180 degrees apart (i.e. located on opposite sides of theillumination axis 102). In the present example, the highest scatteringangle detector element 107 a is above the beam axis, the next element107 b is below, the next element 107 c is above, and so on. The detectorelements in this example are annular, and span an angle about theillumination axis 102 of at least 100 degrees, but less than 180degrees. Spatially separating the detector elements associated withadjacent scattering angles reduces electrical crosstalk and improves theoverall fidelity of the measurement data 108.

The detector elements 107 a-f may be centred on scattering anglescorresponding with a logarithmic sequence, and successive detectorelements may increase in width (extent in scattering angle)logarithmically as the scattering angle increases. Near to theillumination axis 102, at small scattering angles, there may be manyclosely spaced detectors, and at larger scattering angles there may befewer but larger detectors. Such an arrangement may be advantageous,because larger particles produce a higher intensity of scattered lightthat is at low scattering angles, and smaller particles produce areduced intensity of scattered light that is more isotropic (i.e.includes high scattering angles).

The measurement data from the detector 107 may arranged in scatteringangle order, providing a continuous and smooth scattered energydistribution as a particle traverses the laser beam. The intensity withrespect to time and scattering angle may be used to create a3-dimensional visualization of the scattering peak from a particle as ittraverses the illumination beam. Despite the alternating detectorlocations, such a scattering peak will be smooth, due to the symmetricscattering about the illumination axis 102. Scattering resulting from adispersant inhomogeneity will instead produce a peak which is not smoothwith respect to scattering angle, because of the separation of thedetector elements for adjacent scattering angles about the illuminationaxis and the asymmetric scattering about the illumination axis.

Although the detector of FIG. 2 may be particularly suitable fordetecting asymmetry in scattered light patterns, any arrangement ofdetector elements that is capable of detecting asymmetry about theillumination axis is also suitable (for example a 2D array of pixelelements).

FIG. 3 illustrates that measurement of an angle of separation 50 aboutthe illumination axis 102, with reference to a first detector element107 a and a second detector element 107 b. The angle of separation 50 isdetermined with reference to an angle between a first vector between theillumination axis 102 and a centroid of the first detector element 107 aand a second vector between the illumination axis 102 and a centroid ofthe second detector element 107 b. The first and second sensor element107 a, 107 b are not necessarily at the same location along theillumination axis: if the vectors are offset along the illumination axisthe angle can be determined with reference to the projection of thevectors onto a virtual plane that is normal to the illumination axis.

Returning to FIG. 1, the detector 107 is operable to detect thescattered light 106, and to output measurement data 108, which mayprovide a time history of the intensity of scattered light at eachdetector element. The measurement data 108 is provided to a processor110.

The processor 110 executes instructions that may be loaded into amemory. The processing device 110 may include any suitable number(s) andtype(s) of processors or other devices in any suitable arrangement.Example types of processing devices 110 include microprocessors,microcontrollers, digital signal processors, field programmable gatearrays, application specific integrated circuits, and discretecircuitry.

The memory represents any structure(s) capable of storing andfacilitating retrieval of information (such as data, program code,and/or other suitable information on a temporary or permanent basis).The memory may represent a random access memory, read only memory, harddrive, Flash memory, optical disc, or any other suitable volatile ornon-volatile storage device(s).

The processor 110 is configured to identify a measurement contributionarising from light scattered by inhomogeneities in the dispersant 104 byidentifying measured scattered light that is anisotropic about theillumination axis, and to process the measurement data 108 to removethis measurement contribution. The processor 110 subsequently calculatesa PSD from the processed measurement data.

In the present example, the processor 110 is configured to identifypeaks (at 121) in the measurement data 108, and to subsequently ascribea type to each peak (at 122): a particle peak, arising from lightscattered from a particle; or a spurious peak, arising from scatteringfrom a dispersant inhomogeneity. The processor 110 processes themeasurement data 108 to suppress the spurious peaks (as 123), and thendetermines a PSD from the processed measurement data (at 124).

Peaks in time resolved data 108 may be identified by comparing the datafor each detector element with a moving average of the data for thatdetector element.

The moving average μ(t) at time t may be calculated as:

${\mu_{j}(t)} = \frac{\int_{t - {\Delta \; t}}^{t}{{I_{j}(t)}{dt}}}{\Delta \; t}$

where Δt is the length of the time window over which the integration iscalculated and I_(j)(t) the intensity on detector element j.

The standard deviation σ_(j)(t) at time t may be calculated as:

${\sigma_{j}(t)} = \sqrt{{\int_{t - {\Delta \; t}}^{t}{{I_{j}^{2}(t)}{dt}}} - {\mu_{j}^{2}(t)}}$

where Δt should be the same window used to calculate μ(t).

The moving average value may be compared to the raw data by using anormalised difference, Δ_(j)(t):

${\Delta_{j}(t)} = \frac{{{I_{j}(t)} - {\mu_{j}(t)}}}{\mu_{j}(t)}$

and compared to a criteria value C. Alternatively, a z-score, Δ′_(j)(t)can be used to compare the moving average to the raw data:

${\Delta_{j}^{\prime}(t)} = \frac{{I_{j}(t)} - {\mu_{j}(t)}}{\sigma_{j}(t)}$

If Δ_(j)(t)>C or Δ′_(j)(t) >C then the maximum point in the range t tot+Δt is a peak in the data, caused either by a particle or aninhomogeneity of the refractive index of the suspending medium.

The parameter Δt defines the sensitivity of the method to the life-timeof the peak in the raw measurement data, which may be dependent on theviscosity of the dispersant and the hydrodynamic coupling between theparticles 105 and the dispersant 105. If the peak has a life time largerthan Δt then it will not be detected. To avoid failing to detect peaksarising from slow moving particles, several moving averages may be usedwith different window sizes. To improve the speed of peakidentification, the moving averages may be calculated in parallel.

The moving average smooths out the smaller fluctuations so that thecomparison Δ_(j)(t) can be calculated. In general, any smoothingalgorithm may be used. For example, another simple smoothing method thatmight be used is exponential smoothing, the simplest form of which wouldbe

s _(j)(t)=αI _(j)(t)+(1−α)s _(j)(t−δt)

where s_(j)(t) is the smoothed data on detector element j, 0<α<1 is thesmoothing factor and δt is the time step of the detector element. In thecomparison defined by Δ_(j)(t) the function s_(j)(t) would replace thefunction μ_(j)(t). Other examples of smoothing functions includeautoregressive moving averages and autoregressive integrated movingaverages.

Next, the identified peaks are ascribed as either particle peaks, due tolight scattering from particles; or spurious peaks, arising from lightscattering due to dispersant inhomogeneity (resulting in refractiveindex variations). The light scattering due to particles occurs overangular ranges broad enough to extend over multiple detector elements,and is generally sufficiently symmetric about the central point of thedetector 107 to be detected across a continuous range of detectorelements (in terms of scattering angle progression).

This means that one way to characterize particle peaks is to check forpeaks within the range t to t+Δt over n detectors (n>1) and if presentcan attribute the cause of the peak to light scattering from particles.If this is not the case then a peak is categorised as a spurious peak,that is due to refractive index inhomogeneities.

The life time of the peaks may be determined as well as their position.With this extra information spurious peaks could be removed from thedata and the data stitched back together. Another method of spuriouspeak identification would be to detect peaks in the backgroundmeasurement phase, when no particles are present, as described above.All of these peaks would be spurious peaks (since no particles arepresent). The spurious peaks could be statistically analyzed todetermine a typical life time of a spurious peak, and this could becompared with the peaks identified during the measurement. Those peaksthat match the characteristics of spurious peaks could be categorized asdue to refractive index inhomogeneities of the dispersant.

In some embodiments, measurement data 108 may be processed to remove ameasurement contribution arising from light scattering from dispersantinhomogeneities. One way to remove the data is to exclude measurementdata from detector elements corresponding with spurious peaks.

An alternative to identifying peaks in the data is to perform afrequency analysis on scattering data from a sample with substantiallyno particles (and an identical or representative dispersant). A filtermay be generated based on the frequency analysis, to filter outfluctuations from the detector elements arising from dispersantinhomogeneities (as the data is obtained, or as a post-processingoperation).

In some embodiments, a combination of asymmetry and temporalcharacteristics may be used to remove or separate a scatteringcontribution arising from dispersant inhomogeneities. For example, theoutput from the data may be filtered (to remove data with certaintemporal characteristics) and then processed to identify asymmetricscattering. Alternatively, spurious peaks may be identified based on acombination of temporal and symmetry criteria. In some embodiments, bothpredetermined temporal characteristics and asymmetric scattering may berequired to reject a peak as spurious.

FIG. 4 illustrates example scattering measurement data from a particle.Graphs 201-206 each display measurement data from a sequence of sixdetector elements that span a range of scattering angles. At least someof these six detectors are arranged in a different radial location aboutthe illumination axis to at least some of the other detectors.Specifically, in this example, detectors providing odd numbered graphs201, 203, 205, are on the opposite side of the illumination axis 102 tothe detectors providing the even numbered graphs 202, 204, 206.

Each graph 201-206 includes raw measurement data 211 and a movingaverage 212. A peak 220 is detected in the first graph, due to thedifference between the raw data 211 and moving average exceeding athreshold 231. The peak 220 has a maximum 232. A time window t_(w) maybe calculated around the maximum, with lower bound 233 and upper bound234. In each of the other graphs 202-206, a peak is found within thistime window. The peaks in each of these graphs 220 can therefore becategorised as particle peaks, resulting from light scattering from aparticle.

Other criteria may be used to search for corresponding peaks. Forexample, the time window t_(w) could start from the moment that thethreshold is exceeded in graph 201, until the moment the threshold isnot exceeded. The number of adjacent detectors to be checked toestablish that the peak is a particle peak may be any appropriate number(e.g. 2, 3, 4, 5, 10, etc.), in this example 6.

FIG. 5 shows example scattering measurement data from a dispersantinhomogeneity. A peak 220 is identified in the first graph 201, but nocorresponding peak is present in the second graph 202, or any of theother graphs 203 to 206. The peak 220 is therefore categorized as aspurious peak. The measurement data may be processed to eliminate thespurious peak, for example by using the moving average value as the datafor the duration of the spurious peak.

The ability to categorize peaks in the measurement data 108 on the basisof source (spurious or particle) allows several applications, the mostimportant of which is isolated scattering data associated withparticles. In doing so, spurious sizes will no longer be reported to theuser of the particle sizing instrument. In general, the signal-to-noiseratio is also improved, so that accuracy of a PSD is improved.

The ability to separate data in the way described above may have severalother applications. These include a “smart clean” application that wouldcheck for the presence and type of contaminants and then apply theappropriate cleaning procedure or to remove or reduce signals arisingfrom the contaminants by algorithmic means. A “reduced sample size” modemay also be enabled, in which the required amount of sample is reducedbecause sources of noise could be removed from the data. The detectionof bubbles in the dispersant could also be arranged and trigger adegassing procedure.

Another possible application would be a dynamic background monitor,which would allow the background to be monitored during a samplemeasurement rather than only before the measurement begins. The laserlight used to illuminate the particles in the cell also produces aconstant signal across each pixel, which is called the background. Thisis measured before the sample is measured and subtracted from thescattering values recorded by the detector after the sample is measured,which prevents the background influencing the PSD. It's possible thatlong time scale variations of this background during the measurement ofthe sample cause the subtraction of the background to be inaccurate. Bymonitoring the background during the measurement, a real time backgroundmay be subtracted. More accurate monitoring of the background is enabledwhen spurious peaks can be subtracted from the measurement data.

Although specific examples have been described, these are not intendedto be limiting, and the skilled person will understand that furthervariations are possible within the scope of the invention, which isdefined by the appended claims.

1. A method of characterizing particles suspended in a fluid dispersantby light diffraction, comprising: obtaining measurement data from adetector element arranged to measure the intensity of scattered light;identifying a measurement contribution arising from light scattered byinhomogeneities in the dispersant; processing the measurement data toremove or separate the measurement contribution arising from lightscattered by inhomogeneities in the dispersant; and calculating aparticle size distribution from the processed measurement, whereinidentifying a measurement contribution arising from light scattered byinhomogeneities in the dispersant comprises identifying measuredscattered light with predetermined temporal characteristics.
 2. Themethod of claim 1 wherein the predetermined temporal characteristics arederived from temporal characteristics of peaks.
 3. The method of claim2, wherein the temporal characteristics of peaks are derived frommeasurements performed on dispersant that is substantially free fromparticles.
 4. The method of claim 2 wherein the predetermined temporalcharacteristics are derived from a distribution of peak durations. 5.The method of claim 2, wherein the predetermined temporalcharacteristics include a mean and a number of standard deviations usedto define a range of typical spurious peak durations for use asrejection criteria.
 6. The method of claim 2, further comprisingclassifying a peak as a spurious peak resulting from scattering fromdispersant inhomogeneities when its temporal characteristics match thepredetermined temporal characteristics.
 7. The method of claim 1,wherein obtaining a measurement comprises obtaining a time history ofthe intensity of scattered light from the detector element.
 8. Themethod of claim 1, further comprising performing a diffractionexperiment to obtain the measurement data.
 9. A processor or instrumentconfigured to perform the method of claim
 1. 10. A machine readable,non-transient storage medium, comprising instructions for configuring aprocessor or instrument to perform the method according to claim 1.